Token Cap Analysis

This post helps in analyzing crypto tokens bucketting them by their market cap categorization and compare the following in absolute and relative terms 

[1] - How valued are they in the market
[2] - How actively are they traded
[3] - How concentrated are they hodl'ed

Data

We leverage data sources from CoinMarketCap and Ethplorer for this analysis.

In [1]:
import pandas as pd
import numpy as np
import requests
import os
import altair as alt
import warnings
from pandas import json_normalize
import plotly.express as px


warnings.filterwarnings("ignore")
pd.set_option("display.float_format", lambda x: "%.2f" % x)
pd.set_option("display.max_columns", 50)
alt.renderers.enable("default")
# alt.renderers.enable('altair_viewer')
Out[1]:
RendererRegistry.enable('default')

Fetching Latest Listings and categorize tokens by market cap¶

We use CoinMarketCap API to pull top 1000 * listed tokens

We will use their latest/listings endpoint under Cryptocurrency category of API's

*top ranked by coinmarketcap

In [2]:
COINMARKETCAP_API_KEY = os.environ.get("COINMARKETCAP_API_KEY")
URL = "https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest"
PARAMS = {
    "start": 1,
    "limit": 1000,
    "aux": "num_market_pairs,cmc_rank,date_added,tags,platform,max_supply,circulating_supply,total_supply,market_cap_by_total_supply,volume_24h_reported,volume_7d,volume_7d_reported,volume_30d,volume_30d_reported,is_market_cap_included_in_calc",
    "CMC_PRO_API_KEY": COINMARKETCAP_API_KEY,
}

response = requests.get(url=URL, params=PARAMS)


df = json_normalize(response.json(), "data")
In [3]:
df.shape
Out[3]:
(1000, 42)

This endpoint broadly gives 4 types of data

[1] Token's info - its symbol, token address, platform etc

[2] Tokens supply and market cap

[3] Volume traded

[4] Trend of volume traded for a window of 24hrs, 7, 30, 60 and 90 days

In [4]:
df.head(5)
Out[4]:
id name symbol slug num_market_pairs date_added tags max_supply circulating_supply total_supply is_market_cap_included_in_calc platform cmc_rank self_reported_circulating_supply self_reported_market_cap tvl_ratio last_updated quote.USD.price quote.USD.volume_24h quote.USD.volume_24h_reported quote.USD.volume_7d quote.USD.volume_7d_reported quote.USD.volume_30d quote.USD.volume_30d_reported quote.USD.volume_change_24h quote.USD.percent_change_1h quote.USD.percent_change_24h quote.USD.percent_change_7d quote.USD.percent_change_30d quote.USD.percent_change_60d quote.USD.percent_change_90d quote.USD.market_cap quote.USD.market_cap_dominance quote.USD.fully_diluted_market_cap quote.USD.tvl quote.USD.market_cap_by_total_supply quote.USD.last_updated platform.id platform.name platform.symbol platform.slug platform.token_address
0 1 Bitcoin BTC bitcoin 9777 2013-04-28T00:00:00.000Z [mineable, pow, sha-256, store-of-value, state... 21000000.00 19181737.00 19181737.00 1 NaN 1 NaN NaN NaN 2022-10-17T14:28:00.000Z 19553.36 26105392035.29 91167084776.74 270502761031.41 917874592028.58 737514966525.49 87702220155056.41 80.48 -0.04 2.22 1.07 -1.96 -16.66 -12.06 375067443519.15 40.06 410620597806.24 NaN 375067443519.15 2022-10-17T14:28:00.000Z NaN NaN NaN NaN NaN
1 1027 Ethereum ETH ethereum 6139 2015-08-07T00:00:00.000Z [pos, smart-contracts, ethereum-ecosystem, coi... NaN 122373863.50 122373863.50 1 NaN 2 NaN NaN NaN 2022-10-17T14:27:00.000Z 1330.85 9870583976.87 45043638368.62 60366886659.34 269290512209.18 296193947881.93 1351483181263.92 65.22 0.67 3.72 1.37 -7.02 -28.50 -13.50 162860855233.54 17.39 162860855233.54 NaN 162860855233.54 2022-10-17T14:27:00.000Z NaN NaN NaN NaN NaN
2 825 Tether USDT tether 40877 2015-02-25T00:00:00.000Z [payments, stablecoin, asset-backed-stablecoin... NaN 68432559804.79 70146125804.24 1 NaN 3 NaN NaN NaN 2022-10-17T14:27:00.000Z 1.00 36341478368.36 146100656120.30 226688836073.61 911761290272.67 1052657452335.71 4230371498325.82 52.99 0.00 0.00 0.00 0.01 0.00 0.01 68438363990.14 7.30 70152075327.64 NaN 70152075327.64 2022-10-17T14:27:00.000Z 1027.00 Ethereum ETH ethereum 0xdac17f958d2ee523a2206206994597c13d831ec7
3 3408 USD Coin USDC usd-coin 6524 2018-10-08T00:00:00.000Z [medium-of-exchange, stablecoin, asset-backed-... NaN 44995513224.72 44995513224.72 1 NaN 4 NaN NaN NaN 2022-10-17T14:27:00.000Z 1.00 3160569147.74 6592379574.54 18809467321.23 40361910871.20 103640711276.33 216179896466.05 37.14 0.00 0.01 -0.00 0.00 0.01 0.04 44998922083.68 4.81 44998922083.68 NaN 44998922083.68 2022-10-17T14:27:00.000Z 1027.00 Ethereum ETH ethereum 0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48
4 1839 BNB BNB bnb 1126 2017-07-25T00:00:00.000Z [marketplace, centralized-exchange, payments, ... 200000000.00 161337261.09 161337261.09 1 NaN 5 NaN NaN NaN 2022-10-17T14:27:00.000Z 274.17 627480239.47 1544132230.07 4176005332.96 9851631907.22 35906745156.56 101276799338.04 24.98 -0.01 1.58 -0.34 -1.10 -10.85 4.35 44233111221.83 4.72 54833100454.28 NaN 44233111221.83 2022-10-17T14:27:00.000Z NaN NaN NaN NaN NaN
In [5]:
df['platform.name'] = np.where(df['platform.name'].isnull(), "Not Available", df['platform.name'])
df['market_cap_BB'] = df['quote.USD.market_cap']/1000000000

Token Caps

In this section we split all tokens into one of 

- Mega Cap  : >200B USD market cap
- Large Cap : 10B USD to 200B USD
- Mid Cap   : 2B USD to 10B USD
- Small Cap : 300M USD to 2B USD
- Micro Cap : 50M USD to 300M USD
- Nano Cap  : <50M

according to standard thresholds of market capitalization. Read here for more on this

In [6]:
df['token_cap'] = 'NA'
df['token_cap'] = np.where(df['market_cap_BB'] > 200, 'Mega Cap', df['token_cap'])
df['token_cap'] = np.where((df['market_cap_BB'] > 10) & (df['market_cap_BB'] <= 200), 'Large Cap', df['token_cap'])
df['token_cap'] = np.where((df['market_cap_BB'] > 2) & (df['market_cap_BB'] <= 10), 'Mid Cap', df['token_cap'])
df['token_cap'] = np.where((df['market_cap_BB'] > 0.3) & (df['market_cap_BB'] <= 2), 'Small Cap', df['token_cap'])
df['token_cap'] = np.where((df['market_cap_BB'] > 0.05) & (df['market_cap_BB'] <= 0.3), 'Micro Cap', df['token_cap'])
df['token_cap'] = np.where((df['market_cap_BB'] <= 0.05), 'Nano Cap', df['token_cap'])
In [7]:
df['token_cap'] = pd.Categorical(df['token_cap'], ["Mega Cap", "Large Cap", "Mid Cap", "Small Cap", "Micro Cap", "Nano Cap"])

[1] Tokens by Cap Category¶

In this section, we visualize the number of tokens in each market cap category we created. We also take a cap category and individual token view in the next plot

# Tokens in each market cap category created

In [8]:
bars = (
    alt.Chart(df)
    .mark_bar(cornerRadiusTopLeft=3, cornerRadiusTopRight=3)
    .encode(
        x=alt.X("token_cap:O", sort="y"),
        y="count():Q",
        color="token_cap:N",
        tooltip=["token_cap", "count(token_cap)"],
    )
)

text = bars.mark_text(
    align="left",
    baseline="middle",
    dx=-5,
    dy=-5,  # Nudges text to right so it doesn't appear on top of the bar
).encode(text="count(token_cap):Q")

(bars + text).properties(height=400, width=850)
Out[8]:

96% of the top 1000 tokens are either small cap or of even smaller cap segments

3% of the top 1000 tokens are classified as mid cap

Top 1% tokens could be classified as Large cap. BTC is the only eligible Mega Cap

Tokens by market cap - Granular View

In [9]:
fig = px.treemap(
    df,
    path=[px.Constant("all"), "token_cap", "symbol"],
    values="market_cap_BB",
    color="token_cap",
    color_discrete_map={
        "Mega Cap": "lightgrey",
        "Large Cap": "gold",
        "Mid Cap": "cyan",
        "Small Cap": "Violet",
        "Micro Cap": "red",
        "Nano Cap": "green",
    },
)
fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
fig.show()

  • Large Cap segment is dominated by ETH, USDT.

  • All 8 tokens taken together they command a market cap of 383B USD (compared to BTC alone having a market cap of 366B USD)

  • Mid Cap is dominated by DOGE, MATIC.

  • Its 27 tokens have a combined market cap of 113B USD (~ 1/3rd of Large Cap segment)

  • Small cap has a combined market of ~60B

  • followed by Micro cap tokens and Nano cap having a combined market cap of 27B USD and 11B USD respectively

Note that this is an interactive chart - click on the segment name or a particular token symbol to have a more granular view

[2] Tokens by Vol Traded / Market Cap Ratio¶

  • In this section, we derive the VolTraded/MarketCap Ratio

  • This ratio (Vol/MarketCap) signifies the volume traded in a given period as a proportion of market capitalization

  • We visualize the median ratio for each token cap category

In [10]:
df['24hr_vol_traded_by_market_cap'] = df['quote.USD.volume_24h_reported'] / df['quote.USD.market_cap']
df['7day_vol_traded_by_market_cap'] = df['quote.USD.volume_7d_reported'] / df['quote.USD.market_cap']
df['30day_vol_traded_by_market_cap'] = df['quote.USD.volume_30d_reported'] / df['quote.USD.market_cap']
In [11]:
chart_list = []
for metric in ["24hr_vol_traded_by_market_cap","7day_vol_traded_by_market_cap","30day_vol_traded_by_market_cap"]:

    source = pd.DataFrame(df.groupby("token_cap").agg({metric: "median"})).reset_index()
    source

    bars = (
        alt.Chart(
            source,
            title=f"{metric.split('_')[0]} Median - VolumeTraded/MarketCap Ratio ",
        )
        .mark_bar(cornerRadiusTopLeft=3, cornerRadiusTopRight=3)
        .encode(
            x=alt.X(
                "token_cap:O",
                sort=["Mega Cap","Large Cap","Mid Cap","Small Cap","Micro Cap","Nano Cap"],
            ),
            y=metric,
            color="token_cap:N",
            tooltip=["token_cap", metric],
        )
    )

    text = bars.mark_text(
        align="left",
        baseline="middle",
        dx=-5,
        dy=-5,  # Nudges text to right so it doesn't appear on top of the bar
    ).encode(text=alt.Text(metric, format=".0%"))

    chart = (bars + text).properties(height=400, width=850)

    chart_list.append(chart)

alt.vconcat(*chart_list).configure(numberFormat="%")
Out[11]:

  • One chart for each of last 24 hrs, 7 days, and 30 days median volTrade/MarketCap ratio

  • For all time period for which the ratio is calculated, we see a decreasing trend as we move from Mega Cap to Nano Cap indicating that tokens belonging to smaller caps have lower liquidity compared to the larger ones

Although we see a decreasing trend from Mega to Nano cap tokens, do all tokens conform to this pattern or do we see outlier

The next chart will help identify that

In [12]:
chart_list = []
for metric in ["24hr_vol_traded_by_market_cap","7day_vol_traded_by_market_cap","30day_vol_traded_by_market_cap"]:
    chart = (
        alt.Chart(df, title=f"{metric.split('_')[0]} VolumeTraded/MarketCap Ratio ")
        .mark_boxplot(ticks=True, size=70)
        .encode(
            x=alt.X(
                "token_cap:O",
                title=None,
                axis=alt.Axis(labels=True, ticks=False),
                scale=alt.Scale(padding=1),
                sort=["Mega Cap","Large Cap","Mid Cap","Small Cap","Micro Cap","Nan Cap"],
            ),
            y=alt.Y(metric),
            tooltip=["token_cap", metric],
            color="token_cap",
        )
        .properties(width=800)
    )
    chart_list.append(chart)

alt.vconcat(*chart_list).configure_facet(spacing=0).configure_view(stroke=None)
Out[12]:

Although we see the median volTraded/marketCap ratio is decreasing as we move from Mega Cap to Nano Cap

There are a bunch of tokens in Micro, Nano Cap with higher ratios indicating higher trading activity compared to their peers in their respective market cap categories

[3] Tokens by Concentration of Share in top 100 wallets¶

Similar to the Know Your Token blog post (Top Token Holders), where we analyzed the share of top 100 wallets for a given token

We do the same for each of the 1000 tokens and see the token's share of these top 100 wallets

Note: This info is available only for tokens part of ethereum chain. So the below analysis will be restricted to them only. Also, the below snippet (commented) leverages Ethplorer Top Token Holders endpoint to get the top 100 wallets share of a given token

In [13]:
ETHPLORER_API_KEY = os.environ.get("ETHPLORER_API_KEY")
PARAMS = {"limit": 100, "apiKey": ETHPLORER_API_KEY}

total_rows = df.shape[0]
success_count = 0
for index, row in df.iterrows():
    token_address = row["platform.token_address"]
    URL = f"https://api.ethplorer.io/getTopTokenHolders/{token_address}/"
    try:
        response = requests.get(url=URL, params=PARAMS)
    except Exception as e:
        print(e)
        continue

    if response.status_code == 200:
        success_count += 1
        token_share = json_normalize(response.json(), "holders")
        if not token_share.empty:
            df.loc[index, "top_100_wallets_share"] = token_share["share"].sum()
    if response.status_code == 429:
        print("RATE LIMITED")
        break
    # if index%10 == 0:
    # print(f"completed {index} / {total_rows} | success_count: {success_count}")
In [14]:
df["is_ethereum_chain"] = np.where(df["top_100_wallets_share"].isnull(), "NO", "YES")
df["top_100_wallets_share"] = df["top_100_wallets_share"] / 100
In [15]:
df_ethereum = df[df["is_ethereum_chain"] == "YES"]
metric = "top_100_wallets_share"
source = pd.DataFrame(
    df_ethereum.groupby("token_cap").agg({metric: "mean"})
).reset_index()
source

bars = (
    alt.Chart(
        source,
        title="Median share(%) held by top 100 wallets (median aggregated for tokens in each token cap) ",
    )
    .mark_bar(cornerRadiusTopLeft=3, cornerRadiusTopRight=3)
    .encode(
        x=alt.X(
            "token_cap:O",
            sort=["Mega Cap","Large Cap","Mid Cap","Small Cap","Micro Cap","Nano Cap"],
        ),
        y=metric,
        color="token_cap:N",
        tooltip=["token_cap", metric],
    )
)

text = bars.mark_text(align="left", baseline="middle", dx=-5, dy=-5).encode(
    text=alt.Text(metric, format=".2%")
)

(bars + text).properties(height=400, width=850)
Out[15]:

Top 100 wallets have on average 80% of tokens with them in mid cap segment and that number increases to 93% for Nano Cap tokens

In addition to the market cap and volume trade, concentration of token supply can also help us assess tokens for their liquidity

In [16]:
chart_list = []

metric = "top_100_wallets_share"
chart = (
    alt.Chart(df, title="Top 100 Wallets' share")
    .mark_boxplot(ticks=True, size=70)
    .encode(
        x=alt.X(
            "token_cap:O",
            title=None,
            axis=alt.Axis(labels=True, ticks=False),
            scale=alt.Scale(padding=1),
            sort=["Mega Cap","Large Cap","Mid Cap","Small Cap","Micro Cap","Nano Cap"],
        ),
        y=alt.Y(metric),
        tooltip=["token_cap", metric],
        color="token_cap",
    )
    .properties(width=800)
)
chart_list.append(chart)

alt.vconcat(*chart_list).configure_facet(spacing=0).configure_view(stroke=None)
Out[16]:

Similar to what we observed in the case of VolTraded/MarketCap ratio, there are bunch of tokens with lesser concentration* of the token's overall share among top 100 wallets* in each of the different Cap Categories

[4] Combining all 3 - Market Cap, Volume Traded, Concentration of Tokens¶

In [17]:
chart_list = []
for metric in ["24hr_vol_traded_by_market_cap","7day_vol_traded_by_market_cap","30day_vol_traded_by_market_cap"]:
    for token_cap in ["Mid Cap"]:
        scatter = (
            alt.Chart(
                df_ethereum[df_ethereum["token_cap"] == token_cap],
                title=f"{token_cap} - {metric} vs top 100 Wallets Share in the token",
            )
            .mark_point()
            .encode(
                x="top_100_wallets_share",
                y=metric,
                size="market_cap_BB",
                tooltip=["symbol", metric, "top_100_wallets_share", "token_cap"],
            )
            .properties(width=800)
        )

        vline = (
            alt.Chart(df_ethereum[df_ethereum["token_cap"] == token_cap])
            .mark_rule(color="blue")
            .encode(y=f"median({metric}):Q")
        )

        hline = (
            alt.Chart(df_ethereum[df_ethereum["token_cap"] == token_cap])
            .mark_rule(color="blue")
            .encode(
                x="mean(top_100_wallets_share):Q",
            )
        )

        chart_list.append(scatter + vline + hline)

alt.vconcat(*chart_list).resolve_scale(size="independent")
Out[17]:

  • In this chart, we have filtered only Midcap segment for analysis

  • Tokens share concentration (in the top 100 wallets) in the X axis and VolTraded/MarketCap ratio** in the Y axis.

  • Market Cap is represented by the size of the bubble

  • The vertical line represents the average concentration of token share for Mid Cap segment

  • The horizontal line represents the median VolTraded/MarketCap ratio for Mid Cap segment

  • The top left quadrant represents tokens that are likely held by more number of wallets and also has high trade activity

  • The top right quadrant represents tokens that are mostly held by a select few 100 wallets but with high volume trade activity

  • The bottom left quadrant represents tokens that are likely held by more number of wallets but the trade activity is low

  • The bottom right quadrant represents tokens that are mostly held by a select few 100 wallets and their trade activity is also very low

With this we come to the end of this blog post analyzing top 1000 tokens. Hope you found this analysis useful to get a better understanding of tokens and use this to further your own analysis